https://www.eibifri.com/en/1stgeniusai/AI papers and guides with sources and practical applications. Feed dates indicate when a reference joined the library.enFri, 23 Oct 2026 12:00:00 GMThttps://research.google/blog/analyzing-technical-documents-with-deep-review/https://www.eibifri.com/en/1stgeniusai/#material-deep-reviewFri, 23 Oct 2026 12:00:00 GMTGoogle introduces Deep Review for analyzing technical documents, checking arguments, and supporting review. The proposal extends a line of work that began with scientific manuscripts. Application: Try reviewing a public document and compare the notes with a human reading. Limitation: The tool offers assistance, not professional validation. Check data policies and current terms before submitting documents.RadarGoogle Researchhttps://research.google/blog/putting-earth-ai-to-work-geospatial-agents-for-our-planets-biggest-challenges/https://www.eibifri.com/en/1stgeniusai/#material-earth-aiFri, 23 Oct 2026 12:00:00 GMTAn experimental agent connects geospatial models, tools, and planning for analyses that require multiple steps. It is a concrete example of general AI combined with domain knowledge. Application: See how planning and specialized sources enter the workflow before the answer. Limitation: Experimental access and a specialized domain. Results do not automatically transfer to other businesses.RadarGoogle Researchhttps://www.anthropic.com/research/redesigning-source-controlhttps://www.eibifri.com/en/1stgeniusai/#material-scionFri, 23 Oct 2026 12:00:00 GMTAnthropic describes building an internal version control system for an agent-intensive operation. The case highlights new challenges in infrastructure, code review, and integration. Application: Map your delivery bottleneck before investing in more code generation. Limitation: This is an internal case at an unusual scale, not a recommendation to abandon Git.RadarAnthropichttps://www.anthropic.com/engineering/building-effective-agentshttps://www.eibifri.com/en/1stgeniusai/#material-agents-effectiveFri, 23 Oct 2026 12:00:00 GMTA guide to workflows, agents, and composition patterns. It helps you choose the right level of autonomy for a task and start with an understandable architecture. Application: Map the inputs, steps, tools, and stopping criteria for a small task. Limitation: A 2024 guide; the author notes that some tools have changed. Use its principles and check current documentation.Practical guideAnthropichttps://www.anthropic.com/research/writing-tools-for-agentshttps://www.eibifri.com/en/1stgeniusai/#material-toolsFri, 23 Oct 2026 12:00:00 GMTTool names, descriptions, parameters, and responses affect execution quality. This article discusses how to design and evaluate interfaces for agents. Application: Review a tool: make its purpose, parameters, and errors explicit, then test failure cases. Limitation: A well-described interface does not remove the need for permissions and validation.Practical guideAnthropichttps://huggingface.co/docs/smolagents/indexhttps://www.eibifri.com/en/1stgeniusai/#material-smolagentsFri, 23 Oct 2026 12:00:00 GMTDocumentation for a Python library for building agents that use tools or code. It includes getting started material and isolated execution options. Application: Build a prototype with a read-only tool and a well-scoped task. Limitation: Generated code requires isolation. Selected models and services may have their own costs.Practical guideHugging Facehttps://developers.openai.com/api/docs/guides/agent-evalshttps://www.eibifri.com/en/1stgeniusai/#material-agent-evalsFri, 23 Oct 2026 12:00:00 GMTA starting point for evaluating agent workflow behavior, using execution traces, and comparing changes against representative case sets. Application: Record five real failures and turn them into repeatable evaluation cases. Limitation: A guide specific to the OpenAI platform. Check requirements and pricing before adopting it.Practical guideOpenAIhttps://research.google/blog/towards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work/https://www.eibifri.com/en/1stgeniusai/#material-scaling-agentsFri, 23 Oct 2026 12:00:00 GMTAn experimental analysis examines when agent coordination helps and when it adds cost or gets in the way. Task structure matters when choosing an architecture. Application: Compare a simple and a parallel workflow on the same task, with quality and cost defined. Limitation: The results are limited to the study's configurations and tests.Practical guideGoogle Researchhttps://arxiv.org/abs/2005.11401https://www.eibifri.com/en/1stgeniusai/#material-ragFri, 23 Oct 2026 12:00:00 GMTA foundational work combining a model's memory with external document retrieval. It helps explain the concepts behind answers grounded in a knowledge collection. Application: Separate the problem of finding the right document from the problem of producing the answer. Limitation: A foundational paper. It does not by itself describe current implementation practices and tools.PaperarXivhttps://arxiv.org/abs/2210.03629https://www.eibifri.com/en/1stgeniusai/#material-reactFri, 23 Oct 2026 12:00:00 GMTStudies the combination of reasoning and actions with observations from an environment. It is a reference for understanding agents that consult tools throughout a task. Application: Identify when an assistant needs to look for evidence instead of continuing to generate text. Limitation: Results depend on the environments studied. Reasoning explanations do not prove correctness.PaperarXivhttps://arxiv.org/abs/2302.04761https://www.eibifri.com/en/1stgeniusai/#material-toolformerFri, 23 Oct 2026 12:00:00 GMTStudies training that teaches models when to call APIs and incorporate their results. It provides context for the development of models that interact with external tools. Application: Use this reading to distinguish model capability, tool design, and system orchestration. Limitation: This is research on training, not a tutorial for deploying today's agents.PaperarXivhttps://arxiv.org/abs/2405.07437https://www.eibifri.com/en/1stgeniusai/#material-rag-evaluationFri, 23 Oct 2026 12:00:00 GMTOrganizes evaluation dimensions for RAG systems, covering retrieval and generation. It helps structure questions about relevance and faithfulness to sources. Application: Set separate criteria for retrieval, use of evidence, and answer quality. Limitation: Metrics and benchmarks do not represent every risk of real-world use.PaperarXiv